Summary
Present and future global challenges are complex both to understand and to attempt to solve. To comprehend the complex systems underlying these issues, students need systems thinking skills. However, in science education, a uniform delineation of systems thinking across contexts has yet to be established. While there seems to be consensus on several key skills from a theoretical perspective, it remains uncertain whether it is possible to distinguish levels of systems thinking, and if so, how they would be determined. In this study, the impact of the specifics of a system on the skills and levels of systems thinking was investigated.
Research on systems thinking focuses on the understanding of processes and patterns in complex systems. Many studies have described the individual skills necessary to understand complex systems in various fields or contexts, for example natural systems, geographical systems, and biological systems. While all these conceptualizations of systems thinking describe the essential cognitive skills involved in understanding complex systems, in some cases, these individual skills exist side by side and, in others, as skills acquired in a developmental order. Consensus across conceptualizations, however, can be observed, particularly about three central skills of systems thinking: (a) identifying system organization, that is to understand complex systems, students need to identify a complex realm of reality in terms of its organization as a system and be able to describe the relevant components and patterns; (b) analyzing system behavior, which involves the analysis of system development and functional processes as well as the analysis of both direct and indirect cause‐and‐effect relations between the identified elements of the system; (c) system modeling that describes the skills needed to model hypothesized prospective target states of the system. These cognitive skills not only appear in various fields of systems thinking research but also describe systems thinking across fields.
It is unclear how systems thinking skills relate to each other. They are sometimes considered to be hierarchical, while other times they are assumed to exist side by side. Accordingly, it remains to be clarified where and when different levels of sophistication in systems thinking should be considered. The question then arises of how these gradual learning steps appear and how levels of systems thinking can be defined. A “system thinking hierarchic (STH) multilayered model” is being defined by three systems thinking cognitive skills based on a series of independent curricular interventions in the context of earth science education: (a) analysis of system components, (b) synthesis of system components, and (c) problem‐solving. The STH model conceptualizes levels of systems thinking by hierarchically ranking the three cognitive skills in the above order. The application and comparison of the STH model across fields revealed that the development of students' system thinking depends on the content of the system. In biology, but not in earth science, students tended to focus on structural rather than procedural effects, which resulted in a low level of systems thinking. This indicates the existence of system‐specific differences in the difficulty of these cognitive requirements.
A learning progression for systems thinking proposes that the sophistication of students' systems thinking increased as their reasoning regarding complex ecosystems developed from “practical causal reasoning” to “complex causal reasoning”. The initial stage of the resulting learning progression indicates simple cause‐and‐effect reasoning that also contains some scientifically incorrect assumptions. As the learning progression shows, students' systems thinking proceeds from simple cause‐and‐effect to complex reasoning by incorporating an increasing number of elements and relations. Thus, the learning progression assumes a gradual increase in students' systems thinking. A third model of systems thinking incorporates the three central skills: (a) identifying system organization, (b) analyzing system behavior, and (c) system modeling. Contrary to the other two approaches, this model comprises a set of three systems thinking skills arranged side by side, each with several levels of sophistication. Furthermore, an advanced approach to consider system complexity by differentiating levels of systems thinking, which they called stages, is included.
Authors used a quantitative approach to precisely determine the specific influence of two system characteristics on students' understanding of complex systems. They applied the third model to the context of ecology. This enabled them to identify whether the distinct levels of systems thinking differ because of (a) differences in complexity or (b) system specifics, in this case the type of relation. A 36‐item multiple‐choice test was administered to 196 Grade 5 and 6 students. Data analysis, using an Item Response Theory approach confirms a set of systems thinking skills that are necessary to understand complex systems in ecology: identifying system organization, analyzing system behavior, and system modeling. The results of this study also indicate that system specifics, such as type of relation within ecosystems (e.g., predator–prey), appear to determine the formation of levels. Students struggled most with the difference between basic, direct cause‐and‐effect relationships and indirect effects. Once they understood the relevance of indirect relationships in moderately complex systems, a further increase in complexity caused little additional difficulty. Accordingly, authors suggest that systems thinking should be examined from a variety of perspectives. To promote interdisciplinary learning, a systems-thinking model that defines key commonalities across fields while leaving space for system specifics is needed.
Relevance for Complex Systems Knowledge
Τhe paper deals with systems thinking, complex systems, and complexity.
According to the paper, systems thinking is an epistemological approach that focuses on the identification, modeling, and prediction of complex systems as entities rather than as isolated phenomena. A system thinking approach is essential for policymakers, governments, researchers, companies, and individuals. This becomes especially apparent in the context of present and future complex social and environmental challenges, as set out in the United Nations Agenda for Sustainable Development.
Authors mention that although the relevance of systems thinking to science education is growing internationally among educational researchers and policymakers, a coherent conceptualization of systems thinking in educational settings across fields is still missing. While specific cognitive activities seem to be relevant to understanding complex systems beyond particular contexts, it is still uncertain whether domain‐general levels of systems thinking exist and how they are to be classified.
Authors also argue that a universal principle of systems is their complex nature, that is both structural and behavioral complexity, revealing linear and nonlinear interactions as well as emergent effects on different levels of organization. Nevertheless, examining the understanding of complex systems should not be approached solely from the perspective of systems thinking. It was found that students' systems thinking integrated content knowledge, students' conceptions, and their understanding of the system's representation. It can therefore be assumed that system‐specific properties significantly influence systems thinking. The question arises whether complexity alone defines levels of sophistication in systems thinking or whether system specifics such as the type of relation within a system also have a relevant influence on students' understanding.
